منصة إيفاد العلمية

فيض الرحمن محمد خليل ابراهيم

إرسال رسالة

التخصص: هندسة مدني

الجامعة: N/A

النقاط:

27.5

معامل الإنتاج البحثي

الخبرات العلمية

محاضر واستاذ جامعي
مهندس وباحث في وزارة الكهرباء/ الشركة العامة لانتاج الطاقة الكهربائية المنطقة الوسطى
محاضر وباحث دولي
مصمم معماري وانشائي
تطبيقات الذكاء الصناعي في الهندسة المدنية

الأبحاث المنشورة

An Extra Tree Regression Model for Discharge Coefficient Prediction: Novel, Practical Applications in the Hydraulic Sector and Future Research Directions

المجلة: Mathematical Problems in Engineering

سنة النشر: 2021

تاريخ النشر: 2021-09-21

Despite modern advances used to estimate the discharge coefficient (Cd), it is still a major challenge for hydraulic engineers to accurately determine Cd for side weirs. In this study, extra tree regression (ETR) was used to predict the Cd of rectangular sharp-crested side weirs depending on hydraulic and geometrical parameters. The prediction capacity of the ETR model was validated with two predictive models, namely, extreme learning machine (ELM) and random forest (RF). The quantitative assessment revealed that the ETR model achieved the highest accuracy in the predictions compared to other applied models, and also, it exhibited excellent agreement between measured and predicted Cd (correlation coefficient is 0.9603). Moreover, the ETR achieved 6.73% and 22.96% higher prediction accuracy in terms of root mean square error in comparison to ELM and RF, respectively. Furthermore, the performed sensitivity analysis shows that the geometrical parameter such as b/B has the most influence on Cd. Overall, the proposed model (ETR) is found to be a suitable, practical, and qualified computer-aid technology for Cd modeling that may contribute to enhance the basic knowledge of hydraulic considerations.

Applying an efficient AI approach for the prediction of bearing capacity of shallow foundations

المجلة: International Conference on Emerging Technology Trends in Internet of Things and Computing

سنة النشر: 2021

تاريخ النشر: 2021-06-06

This study focused on presenting the potential of artificial intelligence (AI) modeling approach to predict the bearing capacity ( ) of shallow foundation. Accurate prediction of ( ) is very significant in geotechnical engineering and the experimental tests, costs, and efforts can be minimized if ( ) is accurately predicted. In this paper, extreme learning machine (ELM) and multiple linear regression (MLR) models are used along with cross-validation (CV) technique to predict the ultimate bearing capacity . The obtained results showed that CV-ELM is superior to the CV-MLR model in terms of producing high accurate estimations of . The CV-MLR produced more forecasted errors, giving a low correlation coefficient (CC) of 0.755 and a lower value of Index of Agreement (WI) of 0.819. Compared to the CV-ELM model, the prediction accuracy of the latter is higher with CC of 0.946, and WI of 0.945. The superiority of the CV-ELM model was measured in terms of MAE and RMSE during the testing phase. The obtained results showed a prediction improvement by 54.84% and 56.75%, for the MAE and RMSE, respectively, using the CV-ELM model over the standard CV-MLR model. Furthermore, uncertainty analysis was conducted and showed that the CV-MLR produced high uncertainty (about 90%). The proposed models in this study showed a robust and applicable computer aid technology for modeling the ultimate bearing capacity of the shallow foundation may contribute to the base knowledge of geotechnical engineering perspective.

An effective predictive model for daily evapotranspiration based on a limited number of meteorological parameters

المجلة: 2021 Third International Sustainability and Resilience Conference: Climate Change

سنة النشر: 2021

تاريخ النشر: 2021-11-15

As temperatures rise globally, parts of the water cycle will likely speed up due to climate change as evapotranspiration rates increase throughout the world. In this study, three models have been applied to predict the daily evapotranspiration (ET o ) over Santaella station, which is located in Spain. The models are Hargreaves-Samani (HS), modified Hargreaves-Samani (MHS), and Group Method of Data Handling neural network (GMDH-NN). These models are developed using very limited data (temperature parameter). The study found that the HS approach provides the poorest prediction, while the GMDH performance was superior to the MHS. Furthermore, the GMDH-NN model showed a prediction improvement of 16.45% in terms of uncertainty at 95% compared to the MHS model. The study also showed that it is possible to efficiently predict the ET o using a very limited number of meteorological parameters.

Employing a robust data-driven model to assess the environmental damages caused by installing grouted columns

المجلة: 2021 Third International Sustainability and Resilience Conference: Climate Change

سنة النشر: 2021

تاريخ النشر: 2021-11-15

The jet grouting process involves injecting large quantities of highly pressurized fluids into the soil, which may result in a substantial ground displacement and adverse effects on the environment around the excavation. Consequently, the ground displacement must be estimated accurately in the design phase. In this study, two machine learning models namely, extreme learning machine (ELM) and modified K-nearest neighbor (KNN) are used to estimate the ground displacements. The comparison results show that the ELM is superior to the KNN model in terms of estimation accuracy (coefficient of determination is 0.940). Moreover, the ELM model shows an enhancement by 11.43% higher accuracy in terms of reducing the mean absolute error compared to the KNN model. Overall, the results indicate that ELM has the ability to accurately assess the harmful damages caused by installing grouted columns.

Optimising the selection of input variables to increase the predicting accuracy of shear strength for deep beams

المجلة: Complexity

سنة النشر: 2022

تاريخ النشر: 2022-06-04

The deep beam in load transfer is very important as well as difficult to design due to its shear stress problems. Accurate estimation of shear stress would help engineers to get a safer design. One of the major obstacles in building an accurate prediction model is optimising the input variables. Therefore, developing an efficient algorithm to select the optimal input parameters that have the highest information content to represent the target and minimise redundant data is very important. The feature-section algorithm based on the combination of genetic algorithm and information theory (GAITH) was used to select the most important input combinations and introduce them into the prediction models. Four models were used in this study: locally weighted linear regression (LWLR) based on the radial basis kernel function, multiple linear regression (MLR), extreme learning machine (ELM), and random forest (RF). The study found that all applied models were significantly improved by the presence of the GAITH algorithm, except for the MLR model. The LWLR-GAITH model showed 29.15% to 47.88% higher performance accuracy in terms of root mean square error (RMSE) than the other hybrid models during the test phase. Moreover, the results of the standard models (without using the GAITH algorithm) proved the superiority of the LWLR model in reducing the RMSE by 34.51%, 55.17%, and 35.35% compared to RF, MLR, and ELM, respectively. Thus, the inclusion of the LWLR model with GAITH has demonstrated a reliable and applicable computer aid for modelling shear strength in deep beams.

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

المجلة: Advances in Meteorology

سنة النشر: 2022

تاريخ النشر: 2022-09-30

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.

Data-driven models for atmospheric air temperature forecasting at a continental climate region

المجلة: PLOS ONE

سنة النشر: 2023

تاريخ النشر: 2023-11-03

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models’ efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.

Study of Behaviour of Short Concrete Columns Confined with PVC Tube under Uniaxial Load

المجلة: Study of Behaviour of Short Concrete Columns Confined with PVC Tube under Uniaxial Load

سنة النشر: 2022

تاريخ النشر: 2022-11-10

An experimental investigation has been carried out to evaluate the effectiveness of Polyvinyl chloride (PVC) confinements in short plain circular concrete columns. The experimental part is conducted using different PVC tube diameters (110, 160, 220, and 250 mm) with two types of confinement strategies (fully and confined with the cut ends). The results are validated with unconfined samples (control samples). The test results showed that using external confinement of concrete columns by PVC tubes enhances the ultimate load capacity of the short columns. For fully confined samples, the enhancement ratio ranges between 5% and 8.3%, and from 4.16% to 15% for samples with cut ends. Furthermore, the confining of PVC pipes with the cut ends (CCC) has a more considerable effect on load capacity for all diameters except the ones with 250 mm, where the samples with full confinement (Cc ) carried a bigger load than those with cutting ends. Finally, a numerical simulation of samples is carried out by the finite element (FE) model using the ABAQUS software. For all scenarios, the results of the numerical analysis showed considerable similarity to the experimental results, with R2 of 0.95 indicating the high linearity between the actual and simulated compressive strength values. Moreover, the FE induces fewer simulated errors with a relative error (RE) ranging from 0.16% to 6% for all scenarios.

Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques

المجلة: Membranes

سنة النشر: 2023

تاريخ النشر: 2023-12-05

Vacuum membrane distillation (VMD) has attracted increasing interest for various applications besides seawater desalination. Experimental testing of membrane technologies such as VMD on a pilot or large scale can be laborious and costly. Machine learning techniques can be a valuable tool for predicting membrane performance on such scales. In this work, a novel hybrid model was developed based on incorporating a spotted hyena optimizer (SHO) with support vector machine (SVR) to predict the flux pressure in VMD. The SVR–SHO hybrid model was validated with experimental data and benchmarked against other machine learning tools such as artificial neural networks (ANNs), classical SVR, and multiple linear regression (MLR). The results show that the SVR–SHO predicted flux pressure with high accuracy with a correlation coefficient (R) of 0.94. However, other models showed a lower prediction accuracy than SVR–SHO with R-values ranging from 0.801 to 0.902. Global sensitivity analysis was applied to interpret the obtained result, revealing that feed temperature was the most influential operating parameter on flux, with a relative importance score of 52.71 compared to 17.69, 17.16, and 14.44 for feed flowrate, vacuum pressure intensity, and feed concentration, respectively.

Introducing high-order response surface method for improving scour depth prediction downstream of weirs

المجلة: Applied Water Science

سنة النشر: 2024

تاريخ النشر: 2024-05-05

Scour depth downstream of weirs is considered one of the most important hydraulic problems, which greatly influences the stability of weirs. Recently, artificial intelligence (AI) methods have become increasingly popular in modeling hydraulic variables, especially scour depth, because they can capture nonlinear relationships between input variables and their associated objectives. Despite their importance, these models have problems with hyperparameter tuning in scour depth modeling due to their structures, so algorithms must be used to tune the hyperparameters. Moreover, these algorithms are usually tuned by using the trial-and-error method to select the hyperparameters such as the number of hidden nodes, transfer function, and learning rate, and in this case, the main problem is overfitting during the training phase. To solve these problems, the high-order response surface method (HORSM), an improved version of the response surface method (RSM), is used as an alternative approach for the first time in this study to predict the scour depth. The HORSM model is based on high-order polynomial functions (from two to six) compared with the artificial neural network model (ANN). The findings indicate that the fifth order of the HORSM polynomial function yields the most precise predictions, with a higher coefficient of determination (R2) of 0.912 and Willmott Index (WI) of 0.972 compared to the values obtained using ANN (R2 = 0.886 and WI = 0.927). Moreover, the accuracy of the predictions is represented by a reduction of the mean square error by up to 44.17 and 29.01% compared to the classical RSM and ANN, respectively. The suggested model established an excellent correlation and accuracy with experimental values.

Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone

المجلة: Ain Shams Engineering Journal

سنة النشر: 2024

تاريخ النشر: 2024-05-11

The study focuses on developing an accurate prediction model for suspended sediment load (SSL) based on antecedent SSL and water discharge values. Two Artificial Intelligence (AI) models, Hybrid and Parallel, were employed to test on the Kelantan and Mississippi Rivers in different climate zones and river sizes. The parallel model showed better performance than the hybrid in most cases, with the best results based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) (432.06 and 782.15 respectively) for Kelantan and (31672.25 and 62356.60 respectively) for Mississippi. The multifunctional GA neural-network model results have proven its ability to predict SSL in tropical and semi-arid zones. In the Kelantan River, the 8-input combination set was the best prediction model, showing an improvement of more than 38% compared to traditional models. The proposed method has proven to be more accurate than traditional models, ensuring better water resource planning, agricultural management and reservoir operation.